This project attempts to study the cerebral atrophy patterns in gray matter across the different stages of the Alzheimer’s Disease (AD), or more specifically, along the entire AD continuum, in a voxelwise approach. To this ...

To achieve greater efficiency at harvesting tasks, the mechanization of such task is unavoidable. Apart from the mechanical aspects, the harvesting systems needs software that can locate the fruit to be harvested. The use ...

Convolutional neural networks (CNN) are powerful tools for learning representations from im-ages. They are being used in a large range of applications, being the state-of-the art in manycomputer vision tasks. In this work, ...

Convolutional Neural Networks (CNNs) is a technology that is evolving very quickly. The CNNs are used in image recognition to improve the computer vision systems in image classification, object detection and segmentation ...

Convolutional Neural Networks have gained popularity in the recent years due to their performance regarding image analysis, both in classification and segmentation. Especially in the medical field, it is increasingly common ...

[ANGLÈS] The aim of this project has been to create a co-saliency detection algorithm for image pairs based on hierarchical segmentation of those images. As hierarchical segmentation allows multi-scale object representation, ...

[ANGLÈS] The aim of this project has been the creation of visual saliency maps on hierarchical segmentations of an image, to detect prominent objects at different scales and create a final saliency map that highlights the ...

Confocal microscopy is a technology that enables pathologists the rapid analysis of tissue samples for carcinoma detection. However, this technology hasn't established yet in the standard clinical practice because most ...

Given their success, both qualitative and quantitative, Deep Neural Networks have been used to approach classification and segmentation problems for images, especially during these last few years where it has been possible ...